Learning the Abstract General Task Structure in a Rapidly Changing Task Content.
Rapid Instructed Task Learning
instructions-based performance
multilevel modeling
prospective memory
Journal
Journal of cognition
ISSN: 2514-4820
Titre abrégé: J Cogn
Pays: England
ID NLM: 101732790
Informations de publication
Date de publication:
2021
2021
Historique:
received:
23
12
2020
accepted:
17
06
2021
entrez:
19
7
2021
pubmed:
20
7
2021
medline:
20
7
2021
Statut:
epublish
Résumé
The ability to learn abstract generalized structures of tasks is crucial for humans to adapt to changing environments and novel tasks. In a series of five experiments, we investigated this ability using a Rapid Instructed Task Learning paradigm (RITL) comprising short miniblocks, each involving two novel stimulus-response rules. Each miniblock included (a) instructions for the novel stimulus-response rules, (b) a NEXT phase involving a constant (familiar) intervening task (0-5 trials), (c) execution of the newly instructed rules (2 trials). The results show that including a NEXT phase (and hence, a prospective memory demand) led to relatively more robust abstract learning as indicated by increasingly faster responses with experiment progress. Multilevel modeling suggests that the prospective memory demand was just another aspect of the abstract task structure which has been learned.
Identifiants
pubmed: 34278208
doi: 10.5334/joc.176
pmc: PMC8269791
doi:
Types de publication
Journal Article
Langues
eng
Pagination
31Informations de copyright
Copyright: © 2021 The Author(s).
Déclaration de conflit d'intérêts
The authors have no competing interests to declare.
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